Overview

Dataset statistics

Number of variables26
Number of observations18004
Missing cells86954
Missing cells (%)18.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 MiB
Average record size in memory208.0 B

Variable types

Categorical12
Numeric14

Warnings

has_deposit is highly correlated with is_convertedHigh correlation
mifid_money_other_brokers is highly correlated with mifid_invested_other_brokersHigh correlation
mifid_invested_other_brokers is highly correlated with mifid_money_other_brokersHigh correlation
is_converted is highly correlated with has_depositHigh correlation
is_converted is highly correlated with has_depositHigh correlation
has_deposit is highly correlated with is_convertedHigh correlation
finish_mifid_days has 7549 (41.9%) missing values Missing
first_deposit_days has 12948 (71.9%) missing values Missing
linked_account_days has 17376 (96.5%) missing values Missing
demo_account_days has 15289 (84.9%) missing values Missing
demo_trade_days has 15856 (88.1%) missing values Missing
mock_account_days has 17927 (99.6%) missing values Missing
first_deposit_amount is highly skewed (γ1 = 27.52959369) Skewed
start_mifid_days has 12212 (67.8%) zeros Zeros
finish_mifid_days has 2226 (12.4%) zeros Zeros
first_deposit_days has 204 (1.1%) zeros Zeros
first_deposit_amount has 12948 (71.9%) zeros Zeros
first_deposit_platform has 1923 (10.7%) zeros Zeros
mifid_actual_savings has 1548 (8.6%) zeros Zeros
mifid_next_year_savings has 1548 (8.6%) zeros Zeros
mifid_invested_other_brokers has 8277 (46.0%) zeros Zeros
linked_account_days has 303 (1.7%) zeros Zeros
demo_account_days has 2065 (11.5%) zeros Zeros
demo_trade_days has 610 (3.4%) zeros Zeros

Reproduction

Analysis started2021-06-02 18:20:29.982607
Analysis finished2021-06-02 18:21:46.515469
Duration1 minute and 16.53 seconds
Software versionpandas-profiling v2.13.0
Download configurationconfig.yaml

Variables

user_currency
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 KiB
USD
10731 
EUR
6426 
GBP
 
844
NO_CURRENCY
 
3

Length

Max length11
Median length3
Mean length3.001333037
Min length3

Characters and Unicode

Total characters54036
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEUR
2nd rowEUR
3rd rowUSD
4th rowUSD
5th rowUSD
ValueCountFrequency (%)
USD10731
59.6%
EUR6426
35.7%
GBP844
 
4.7%
NO_CURRENCY3
 
< 0.1%
2021-06-02T20:21:47.010947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-02T20:21:47.249119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
usd10731
59.6%
eur6426
35.7%
gbp844
 
4.7%
no_currency3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
U17160
31.8%
S10731
19.9%
D10731
19.9%
R6432
 
11.9%
E6429
 
11.9%
G844
 
1.6%
B844
 
1.6%
P844
 
1.6%
N6
 
< 0.1%
C6
 
< 0.1%
Other values (3)9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter54033
> 99.9%
Connector Punctuation3
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
U17160
31.8%
S10731
19.9%
D10731
19.9%
R6432
 
11.9%
E6429
 
11.9%
G844
 
1.6%
B844
 
1.6%
P844
 
1.6%
N6
 
< 0.1%
C6
 
< 0.1%
Other values (2)6
 
< 0.1%
ValueCountFrequency (%)
_3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin54033
> 99.9%
Common3
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
U17160
31.8%
S10731
19.9%
D10731
19.9%
R6432
 
11.9%
E6429
 
11.9%
G844
 
1.6%
B844
 
1.6%
P844
 
1.6%
N6
 
< 0.1%
C6
 
< 0.1%
Other values (2)6
 
< 0.1%
ValueCountFrequency (%)
_3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII54036
100.0%

Most frequent character per block

ValueCountFrequency (%)
U17160
31.8%
S10731
19.9%
D10731
19.9%
R6432
 
11.9%
E6429
 
11.9%
G844
 
1.6%
B844
 
1.6%
P844
 
1.6%
N6
 
< 0.1%
C6
 
< 0.1%
Other values (3)9
 
< 0.1%

user_country
Real number (ℝ≥0)

Distinct162
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.27255054
Minimum0
Maximum161
Zeros32
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size140.8 KiB
2021-06-02T20:21:47.520427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q132
median46
Q3100
95-th percentile160
Maximum161
Range161
Interquartile range (IQR)68

Descriptive statistics

Standard deviation44.54543168
Coefficient of variation (CV)0.6721550826
Kurtosis-0.4137371428
Mean66.27255054
Median Absolute Deviation (MAD)16
Skewness0.874743413
Sum1193171
Variance1984.295484
MonotonicityNot monotonic
2021-06-02T20:21:47.843335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
464438
24.7%
311240
 
6.9%
531102
 
6.1%
1601101
 
6.1%
100968
 
5.4%
8783
 
4.3%
30569
 
3.2%
111535
 
3.0%
28452
 
2.5%
43411
 
2.3%
Other values (152)6405
35.6%
ValueCountFrequency (%)
032
 
0.2%
1102
0.6%
21
 
< 0.1%
31
 
< 0.1%
47
 
< 0.1%
ValueCountFrequency (%)
161298
 
1.7%
1601101
6.1%
1593
 
< 0.1%
1581
 
< 0.1%
1576
 
< 0.1%

start_mifid_days
Real number (ℝ≥0)

ZEROS

Distinct584
Distinct (%)3.2%
Missing9
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean25.63728814
Minimum0
Maximum1112
Zeros12212
Zeros (%)67.8%
Negative0
Negative (%)0.0%
Memory size140.8 KiB
2021-06-02T20:21:48.205956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile163
Maximum1112
Range1112
Interquartile range (IQR)3

Descriptive statistics

Standard deviation87.79499459
Coefficient of variation (CV)3.424503954
Kurtosis34.78382969
Mean25.63728814
Median Absolute Deviation (MAD)0
Skewness5.347203868
Sum461343
Variance7707.961075
MonotonicityNot monotonic
2021-06-02T20:21:48.538910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012212
67.8%
1853
 
4.7%
2367
 
2.0%
3250
 
1.4%
4231
 
1.3%
5141
 
0.8%
7135
 
0.7%
6120
 
0.7%
8107
 
0.6%
1183
 
0.5%
Other values (574)3496
 
19.4%
ValueCountFrequency (%)
012212
67.8%
1853
 
4.7%
2367
 
2.0%
3250
 
1.4%
4231
 
1.3%
ValueCountFrequency (%)
11121
< 0.1%
10791
< 0.1%
10331
< 0.1%
10191
< 0.1%
9801
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 KiB
1
10454 
0
7550 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18004
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0
ValueCountFrequency (%)
110454
58.1%
07550
41.9%
2021-06-02T20:21:49.155432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-02T20:21:49.451031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
110454
58.1%
07550
41.9%

Most occurring characters

ValueCountFrequency (%)
110454
58.1%
07550
41.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18004
100.0%

Most frequent character per category

ValueCountFrequency (%)
110454
58.1%
07550
41.9%

Most occurring scripts

ValueCountFrequency (%)
Common18004
100.0%

Most frequent character per script

ValueCountFrequency (%)
110454
58.1%
07550
41.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII18004
100.0%

Most frequent character per block

ValueCountFrequency (%)
110454
58.1%
07550
41.9%

finish_mifid_days
Real number (ℝ≥0)

MISSING
ZEROS

Distinct580
Distinct (%)5.5%
Missing7549
Missing (%)41.9%
Infinite0
Infinite (%)0.0%
Mean43.18278336
Minimum0
Maximum1114
Zeros2226
Zeros (%)12.4%
Negative0
Negative (%)0.0%
Memory size140.8 KiB
2021-06-02T20:21:49.672601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q321
95-th percentile265
Maximum1114
Range1114
Interquartile range (IQR)20

Descriptive statistics

Standard deviation111.319832
Coefficient of variation (CV)2.577875333
Kurtosis20.12343614
Mean43.18278336
Median Absolute Deviation (MAD)3
Skewness4.11168237
Sum451476
Variance12392.105
MonotonicityNot monotonic
2021-06-02T20:21:50.011854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02226
 
12.4%
12028
 
11.3%
2899
 
5.0%
3570
 
3.2%
4368
 
2.0%
5274
 
1.5%
6206
 
1.1%
7183
 
1.0%
8146
 
0.8%
9101
 
0.6%
Other values (570)3454
19.2%
(Missing)7549
41.9%
ValueCountFrequency (%)
02226
12.4%
12028
11.3%
2899
5.0%
3570
 
3.2%
4368
 
2.0%
ValueCountFrequency (%)
11141
< 0.1%
10851
< 0.1%
10331
< 0.1%
10191
< 0.1%
9801
< 0.1%

has_deposit
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 KiB
0
12948 
1
5056 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18004
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
012948
71.9%
15056
 
28.1%
2021-06-02T20:21:50.657553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-02T20:21:50.850664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
012948
71.9%
15056
 
28.1%

Most occurring characters

ValueCountFrequency (%)
012948
71.9%
15056
 
28.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18004
100.0%

Most frequent character per category

ValueCountFrequency (%)
012948
71.9%
15056
 
28.1%

Most occurring scripts

ValueCountFrequency (%)
Common18004
100.0%

Most frequent character per script

ValueCountFrequency (%)
012948
71.9%
15056
 
28.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII18004
100.0%

Most frequent character per block

ValueCountFrequency (%)
012948
71.9%
15056
 
28.1%

first_deposit_days
Real number (ℝ≥0)

MISSING
ZEROS

Distinct540
Distinct (%)10.7%
Missing12948
Missing (%)71.9%
Infinite0
Infinite (%)0.0%
Mean80.04786392
Minimum0
Maximum1105
Zeros204
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size140.8 KiB
2021-06-02T20:21:51.078217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median18
Q382
95-th percentile392.25
Maximum1105
Range1105
Interquartile range (IQR)77

Descriptive statistics

Standard deviation142.6826422
Coefficient of variation (CV)1.782466579
Kurtosis9.780455233
Mean80.04786392
Median Absolute Deviation (MAD)16
Skewness2.911367192
Sum404722
Variance20358.33638
MonotonicityNot monotonic
2021-06-02T20:21:51.418865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1289
 
1.6%
3247
 
1.4%
2242
 
1.3%
0204
 
1.1%
4197
 
1.1%
6184
 
1.0%
5177
 
1.0%
7148
 
0.8%
8119
 
0.7%
9116
 
0.6%
Other values (530)3133
 
17.4%
(Missing)12948
71.9%
ValueCountFrequency (%)
0204
1.1%
1289
1.6%
2242
1.3%
3247
1.4%
4197
1.1%
ValueCountFrequency (%)
11051
< 0.1%
10851
< 0.1%
10441
< 0.1%
10191
< 0.1%
9831
< 0.1%

first_deposit_amount
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct671
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.904987805
Minimum0
Maximum1000
Zeros12948
Zeros (%)71.9%
Negative0
Negative (%)0.0%
Memory size140.8 KiB
2021-06-02T20:21:51.767142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile12
Maximum1000
Range1000
Interquartile range (IQR)2

Descriptive statistics

Standard deviation17.75249612
Coefficient of variation (CV)6.111039808
Kurtosis1167.017172
Mean2.904987805
Median Absolute Deviation (MAD)0
Skewness27.52959369
Sum52301.40044
Variance315.1511184
MonotonicityNot monotonic
2021-06-02T20:21:52.129621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012948
71.9%
21754
 
9.7%
4880
 
4.9%
8258
 
1.4%
20199
 
1.1%
2.4131
 
0.7%
40124
 
0.7%
12122
 
0.7%
6102
 
0.6%
1068
 
0.4%
Other values (661)1418
 
7.9%
ValueCountFrequency (%)
012948
71.9%
0.011161
 
< 0.1%
0.021921
 
< 0.1%
0.0281
 
< 0.1%
0.030361
 
< 0.1%
ValueCountFrequency (%)
10001
< 0.1%
879.7541
< 0.1%
739.381
< 0.1%
4002
< 0.1%
399.9741
< 0.1%

first_deposit_platform
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.467396134
Minimum0
Maximum6
Zeros1923
Zeros (%)10.7%
Negative0
Negative (%)0.0%
Memory size140.8 KiB
2021-06-02T20:21:52.427774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.394194913
Coefficient of variation (CV)0.9501148875
Kurtosis2.01614035
Mean1.467396134
Median Absolute Deviation (MAD)0
Skewness1.785887667
Sum26419
Variance1.943779455
MonotonicityNot monotonic
2021-06-02T20:21:52.659877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
112948
71.9%
01923
 
10.7%
51685
 
9.4%
3916
 
5.1%
4389
 
2.2%
6114
 
0.6%
229
 
0.2%
ValueCountFrequency (%)
01923
 
10.7%
112948
71.9%
229
 
0.2%
3916
 
5.1%
4389
 
2.2%
ValueCountFrequency (%)
6114
 
0.6%
51685
9.4%
4389
 
2.2%
3916
5.1%
229
 
0.2%

mifid_actual_savings
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.924850033
Minimum0
Maximum15
Zeros1548
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size140.8 KiB
2021-06-02T20:21:52.904439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median10
Q312
95-th percentile13
Maximum15
Range15
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.993755347
Coefficient of variation (CV)0.4474871098
Kurtosis-0.3709338517
Mean8.924850033
Median Absolute Deviation (MAD)3
Skewness-0.7651481344
Sum160683
Variance15.95008177
MonotonicityNot monotonic
2021-06-02T20:21:53.155171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
125852
32.5%
132788
15.5%
52116
 
11.8%
61684
 
9.4%
71609
 
8.9%
01548
 
8.6%
81125
 
6.2%
9674
 
3.7%
10294
 
1.6%
15202
 
1.1%
ValueCountFrequency (%)
01548
8.6%
52116
11.8%
61684
9.4%
71609
8.9%
81125
6.2%
ValueCountFrequency (%)
15202
 
1.1%
132788
15.5%
125852
32.5%
11112
 
0.6%
10294
 
1.6%

mifid_next_year_savings
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.462563875
Minimum0
Maximum15
Zeros1548
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size140.8 KiB
2021-06-02T20:21:53.409279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median8
Q312
95-th percentile13
Maximum15
Range15
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.989778698
Coefficient of variation (CV)0.4714621664
Kurtosis-0.640226688
Mean8.462563875
Median Absolute Deviation (MAD)3
Skewness-0.4852894058
Sum152360
Variance15.91833406
MonotonicityNot monotonic
2021-06-02T20:21:53.649688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
123964
22.0%
133204
17.8%
52718
15.1%
62105
11.7%
71929
10.7%
01548
 
8.6%
81231
 
6.8%
9634
 
3.5%
10306
 
1.7%
15197
 
1.1%
ValueCountFrequency (%)
01548
8.6%
52718
15.1%
62105
11.7%
71929
10.7%
81231
6.8%
ValueCountFrequency (%)
15197
 
1.1%
133204
17.8%
123964
22.0%
11168
 
0.9%
10306
 
1.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 KiB
1
10876 
0
7128 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18004
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0
ValueCountFrequency (%)
110876
60.4%
07128
39.6%
2021-06-02T20:21:54.242145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-02T20:21:54.407815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
110876
60.4%
07128
39.6%

Most occurring characters

ValueCountFrequency (%)
110876
60.4%
07128
39.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18004
100.0%

Most frequent character per category

ValueCountFrequency (%)
110876
60.4%
07128
39.6%

Most occurring scripts

ValueCountFrequency (%)
Common18004
100.0%

Most frequent character per script

ValueCountFrequency (%)
110876
60.4%
07128
39.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII18004
100.0%

Most frequent character per block

ValueCountFrequency (%)
110876
60.4%
07128
39.6%

mifid_money_other_brokers
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 KiB
1
9728 
0
8276 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18004
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0
ValueCountFrequency (%)
19728
54.0%
08276
46.0%
2021-06-02T20:21:54.906673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-02T20:21:55.191199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
19728
54.0%
08276
46.0%

Most occurring characters

ValueCountFrequency (%)
19728
54.0%
08276
46.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18004
100.0%

Most frequent character per category

ValueCountFrequency (%)
19728
54.0%
08276
46.0%

Most occurring scripts

ValueCountFrequency (%)
Common18004
100.0%

Most frequent character per script

ValueCountFrequency (%)
19728
54.0%
08276
46.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII18004
100.0%

Most frequent character per block

ValueCountFrequency (%)
19728
54.0%
08276
46.0%

mifid_invested_other_brokers
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.519828927
Minimum0
Maximum15
Zeros8277
Zeros (%)46.0%
Negative0
Negative (%)0.0%
Memory size140.8 KiB
2021-06-02T20:21:55.373717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q312
95-th percentile13
Maximum15
Range15
Interquartile range (IQR)12

Descriptive statistics

Standard deviation5.532329734
Coefficient of variation (CV)1.002264709
Kurtosis-1.732979537
Mean5.519828927
Median Absolute Deviation (MAD)5
Skewness0.2007539217
Sum99379
Variance30.60667228
MonotonicityNot monotonic
2021-06-02T20:21:55.636518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
08277
46.0%
124659
25.9%
131581
 
8.8%
51195
 
6.6%
6808
 
4.5%
7640
 
3.6%
8441
 
2.4%
9229
 
1.3%
1084
 
0.5%
1549
 
0.3%
ValueCountFrequency (%)
08277
46.0%
51195
 
6.6%
6808
 
4.5%
7640
 
3.6%
8441
 
2.4%
ValueCountFrequency (%)
1549
 
0.3%
131581
 
8.8%
124659
25.9%
1141
 
0.2%
1084
 
0.5%

mifid_experience
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 KiB
0
9936 
1
8068 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18004
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
09936
55.2%
18068
44.8%
2021-06-02T20:21:56.246034image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-02T20:21:56.436474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
09936
55.2%
18068
44.8%

Most occurring characters

ValueCountFrequency (%)
09936
55.2%
18068
44.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18004
100.0%

Most frequent character per category

ValueCountFrequency (%)
09936
55.2%
18068
44.8%

Most occurring scripts

ValueCountFrequency (%)
Common18004
100.0%

Most frequent character per script

ValueCountFrequency (%)
09936
55.2%
18068
44.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII18004
100.0%

Most frequent character per block

ValueCountFrequency (%)
09936
55.2%
18068
44.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 KiB
0
17376 
1
 
628

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18004
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
017376
96.5%
1628
 
3.5%
2021-06-02T20:21:56.942670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-02T20:21:57.115845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
017376
96.5%
1628
 
3.5%

Most occurring characters

ValueCountFrequency (%)
017376
96.5%
1628
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18004
100.0%

Most frequent character per category

ValueCountFrequency (%)
017376
96.5%
1628
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common18004
100.0%

Most frequent character per script

ValueCountFrequency (%)
017376
96.5%
1628
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII18004
100.0%

Most frequent character per block

ValueCountFrequency (%)
017376
96.5%
1628
 
3.5%

linked_account_days
Real number (ℝ≥0)

MISSING
ZEROS

Distinct126
Distinct (%)20.1%
Missing17376
Missing (%)96.5%
Infinite0
Infinite (%)0.0%
Mean35.64171975
Minimum0
Maximum1033
Zeros303
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size140.8 KiB
2021-06-02T20:21:57.342376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q314
95-th percentile231
Maximum1033
Range1033
Interquartile range (IQR)14

Descriptive statistics

Standard deviation102.2977983
Coefficient of variation (CV)2.870170099
Kurtosis30.56520024
Mean35.64171975
Median Absolute Deviation (MAD)1
Skewness4.888651126
Sum22383
Variance10464.83953
MonotonicityNot monotonic
2021-06-02T20:21:57.683731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0303
 
1.7%
153
 
0.3%
230
 
0.2%
328
 
0.2%
414
 
0.1%
68
 
< 0.1%
108
 
< 0.1%
56
 
< 0.1%
175
 
< 0.1%
85
 
< 0.1%
Other values (116)168
 
0.9%
(Missing)17376
96.5%
ValueCountFrequency (%)
0303
1.7%
153
 
0.3%
230
 
0.2%
328
 
0.2%
414
 
0.1%
ValueCountFrequency (%)
10331
< 0.1%
8381
< 0.1%
7281
< 0.1%
6421
< 0.1%
6201
< 0.1%

has_demo_account
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 KiB
0
15289 
1
2715 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18004
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
015289
84.9%
12715
 
15.1%
2021-06-02T20:21:58.296008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-02T20:21:58.479633image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
015289
84.9%
12715
 
15.1%

Most occurring characters

ValueCountFrequency (%)
015289
84.9%
12715
 
15.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18004
100.0%

Most frequent character per category

ValueCountFrequency (%)
015289
84.9%
12715
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
Common18004
100.0%

Most frequent character per script

ValueCountFrequency (%)
015289
84.9%
12715
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII18004
100.0%

Most frequent character per block

ValueCountFrequency (%)
015289
84.9%
12715
 
15.1%

demo_account_days
Real number (ℝ≥0)

MISSING
ZEROS

Distinct166
Distinct (%)6.1%
Missing15289
Missing (%)84.9%
Infinite0
Infinite (%)0.0%
Mean13.5145488
Minimum0
Maximum1019
Zeros2065
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size140.8 KiB
2021-06-02T20:21:58.708848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile50.3
Maximum1019
Range1019
Interquartile range (IQR)0

Descriptive statistics

Standard deviation64.89988939
Coefficient of variation (CV)4.80222391
Kurtosis68.79046175
Mean13.5145488
Median Absolute Deviation (MAD)0
Skewness7.484010319
Sum36692
Variance4211.995643
MonotonicityNot monotonic
2021-06-02T20:21:59.052808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02065
 
11.5%
1109
 
0.6%
253
 
0.3%
443
 
0.2%
340
 
0.2%
531
 
0.2%
723
 
0.1%
622
 
0.1%
919
 
0.1%
1114
 
0.1%
Other values (156)296
 
1.6%
(Missing)15289
84.9%
ValueCountFrequency (%)
02065
11.5%
1109
 
0.6%
253
 
0.3%
340
 
0.2%
443
 
0.2%
ValueCountFrequency (%)
10191
< 0.1%
7921
< 0.1%
7581
< 0.1%
7291
< 0.1%
6711
< 0.1%

has_demo_trade
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 KiB
0
15856 
1
2148 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18004
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
015856
88.1%
12148
 
11.9%
2021-06-02T20:21:59.689978image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-02T20:21:59.872465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
015856
88.1%
12148
 
11.9%

Most occurring characters

ValueCountFrequency (%)
015856
88.1%
12148
 
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18004
100.0%

Most frequent character per category

ValueCountFrequency (%)
015856
88.1%
12148
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
Common18004
100.0%

Most frequent character per script

ValueCountFrequency (%)
015856
88.1%
12148
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII18004
100.0%

Most frequent character per block

ValueCountFrequency (%)
015856
88.1%
12148
 
11.9%

demo_trade_days
Real number (ℝ≥0)

MISSING
ZEROS

Distinct225
Distinct (%)10.5%
Missing15856
Missing (%)88.1%
Infinite0
Infinite (%)0.0%
Mean27.87337058
Minimum0
Maximum1066
Zeros610
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size140.8 KiB
2021-06-02T20:22:00.094434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q313
95-th percentile158.65
Maximum1066
Range1066
Interquartile range (IQR)13

Descriptive statistics

Standard deviation83.21780099
Coefficient of variation (CV)2.985566484
Kurtosis34.4984913
Mean27.87337058
Median Absolute Deviation (MAD)2
Skewness5.222174053
Sum59872
Variance6925.202402
MonotonicityNot monotonic
2021-06-02T20:22:00.436416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0610
 
3.4%
1328
 
1.8%
2169
 
0.9%
3115
 
0.6%
474
 
0.4%
756
 
0.3%
552
 
0.3%
652
 
0.3%
838
 
0.2%
1132
 
0.2%
Other values (215)622
 
3.5%
(Missing)15856
88.1%
ValueCountFrequency (%)
0610
3.4%
1328
1.8%
2169
 
0.9%
3115
 
0.6%
474
 
0.4%
ValueCountFrequency (%)
10661
< 0.1%
7921
< 0.1%
7411
< 0.1%
7291
< 0.1%
6711
< 0.1%

has_mock_account
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 KiB
0
17927 
1
 
77

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18004
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
017927
99.6%
177
 
0.4%
2021-06-02T20:22:01.145990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-02T20:22:01.325465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
017927
99.6%
177
 
0.4%

Most occurring characters

ValueCountFrequency (%)
017927
99.6%
177
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18004
100.0%

Most frequent character per category

ValueCountFrequency (%)
017927
99.6%
177
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common18004
100.0%

Most frequent character per script

ValueCountFrequency (%)
017927
99.6%
177
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII18004
100.0%

Most frequent character per block

ValueCountFrequency (%)
017927
99.6%
177
 
0.4%

mock_account_days
Real number (ℝ≥0)

MISSING

Distinct42
Distinct (%)54.5%
Missing17927
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean71.90909091
Minimum0
Maximum635
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size140.8 KiB
2021-06-02T20:22:01.548695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median12
Q375
95-th percentile336.2
Maximum635
Range635
Interquartile range (IQR)74

Descriptive statistics

Standard deviation122.6312596
Coefficient of variation (CV)1.705365178
Kurtosis6.250862559
Mean71.90909091
Median Absolute Deviation (MAD)12
Skewness2.404544909
Sum5537
Variance15038.42584
MonotonicityNot monotonic
2021-06-02T20:22:01.882676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
013
 
0.1%
18
 
< 0.1%
25
 
< 0.1%
45
 
< 0.1%
33
 
< 0.1%
492
 
< 0.1%
2662
 
< 0.1%
112
 
< 0.1%
52
 
< 0.1%
1522
 
< 0.1%
Other values (32)33
 
0.2%
(Missing)17927
99.6%
ValueCountFrequency (%)
013
0.1%
18
< 0.1%
25
 
< 0.1%
33
 
< 0.1%
45
 
< 0.1%
ValueCountFrequency (%)
6351
< 0.1%
4141
< 0.1%
4011
< 0.1%
3931
< 0.1%
3221
< 0.1%

user_flow_name
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 KiB
2
13406 
0
4420 
1
 
178

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18004
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row0
4th row2
5th row2
ValueCountFrequency (%)
213406
74.5%
04420
 
24.6%
1178
 
1.0%
2021-06-02T20:22:02.513524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-02T20:22:02.703702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
213406
74.5%
04420
 
24.6%
1178
 
1.0%

Most occurring characters

ValueCountFrequency (%)
213406
74.5%
04420
 
24.6%
1178
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18004
100.0%

Most frequent character per category

ValueCountFrequency (%)
213406
74.5%
04420
 
24.6%
1178
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common18004
100.0%

Most frequent character per script

ValueCountFrequency (%)
213406
74.5%
04420
 
24.6%
1178
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII18004
100.0%

Most frequent character per block

ValueCountFrequency (%)
213406
74.5%
04420
 
24.6%
1178
 
1.0%

days_until_conversion_or_today
Real number (ℝ≥0)

Distinct1129
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean405.215952
Minimum0
Maximum1128
Zeros102
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size140.8 KiB
2021-06-02T20:22:02.970697image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q196
median351
Q3685
95-th percentile1016
Maximum1128
Range1128
Interquartile range (IQR)589

Descriptive statistics

Standard deviation336.2311493
Coefficient of variation (CV)0.8297579295
Kurtosis-0.9962257948
Mean405.215952
Median Absolute Deviation (MAD)277
Skewness0.5132921862
Sum7295508
Variance113051.3858
MonotonicityNot monotonic
2021-06-02T20:22:03.291725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3164
 
0.9%
4153
 
0.8%
1151
 
0.8%
6142
 
0.8%
7139
 
0.8%
2133
 
0.7%
5121
 
0.7%
8119
 
0.7%
9104
 
0.6%
0102
 
0.6%
Other values (1119)16676
92.6%
ValueCountFrequency (%)
0102
0.6%
1151
0.8%
2133
0.7%
3164
0.9%
4153
0.8%
ValueCountFrequency (%)
11285
< 0.1%
11274
 
< 0.1%
112610
0.1%
11258
< 0.1%
11245
< 0.1%

is_converted
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 KiB
0
13666 
1
4338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18004
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
013666
75.9%
14338
 
24.1%
2021-06-02T20:22:03.880733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-02T20:22:04.048975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
013666
75.9%
14338
 
24.1%

Most occurring characters

ValueCountFrequency (%)
013666
75.9%
14338
 
24.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18004
100.0%

Most frequent character per category

ValueCountFrequency (%)
013666
75.9%
14338
 
24.1%

Most occurring scripts

ValueCountFrequency (%)
Common18004
100.0%

Most frequent character per script

ValueCountFrequency (%)
013666
75.9%
14338
 
24.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII18004
100.0%

Most frequent character per block

ValueCountFrequency (%)
013666
75.9%
14338
 
24.1%

Interactions

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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-02T20:22:05.230308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
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Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
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Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
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Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-06-02T20:21:42.261436image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-02T20:21:44.659295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-06-02T20:21:45.459814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-06-02T20:21:46.013616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

user_currencyuser_countrystart_mifid_dayshas_finished_mifidfinish_mifid_dayshas_depositfirst_deposit_daysfirst_deposit_amountfirst_deposit_platformmifid_actual_savingsmifid_next_year_savingsmifid_qualificationsmifid_money_other_brokersmifid_invested_other_brokersmifid_experiencehas_linked_accountlinked_account_dayshas_demo_accountdemo_account_dayshas_demo_tradedemo_trade_dayshas_mock_accountmock_account_daysuser_flow_namedays_until_conversion_or_todayis_converted
0EUR460.011.011.02.005611610NaN0NaN0NaN0NaN211
1EUR460.010.012.020.008811600NaN0NaN0NaN0NaN221
2USD434.017.00NaN0.011212111200NaN0NaN0NaN0NaN080
3USD310.00NaN0NaN0.011313011200NaN0NaN0NaN0NaN280
4USD220.00NaN0NaN0.010000000NaN0NaN0NaN0NaN280
5EUR360.011.00NaN0.019711710NaN0NaN0NaN0NaN280
6EUR500.011.00NaN0.019811610NaN0NaN0NaN0NaN280
7USD410.00NaN0NaN0.010000000NaN0NaN0NaN0NaN280
8USD220.010.00NaN0.018611810NaN0NaN0NaN0NaN280
9EUR1530.011.00NaN0.01121200000NaN0NaN0NaN0NaN280

Last rows

user_currencyuser_countrystart_mifid_dayshas_finished_mifidfinish_mifid_dayshas_depositfirst_deposit_daysfirst_deposit_amountfirst_deposit_platformmifid_actual_savingsmifid_next_year_savingsmifid_qualificationsmifid_money_other_brokersmifid_invested_other_brokersmifid_experiencehas_linked_accountlinked_account_dayshas_demo_accountdemo_account_dayshas_demo_tradedemo_trade_dayshas_mock_accountmock_account_daysuser_flow_namedays_until_conversion_or_todayis_converted
17994USD300.00NaN0NaN0.010000000NaN0NaN0NaN0NaN211270
17995USD31206.01229.01380.02.45135111310NaN0NaN0NaN0NaN211270
17996USD3043.00NaN0NaN0.010000000NaN0NaN0NaN0NaN211270
17997USD1601.0131.01393.02.0212121001187.0184.0184.00NaN23941
17998EUR460.011.013.02.05131310010NaN0NaN0NaN0NaN261
17999EUR4679.00NaN0NaN0.010000000NaN0NaN0NaN0NaN211280
18000USD740.00NaN0NaN0.015710010NaN0NaN0NaN0NaN211280
18001EUR460.00NaN0NaN0.010000000NaN0NaN0NaN0NaN211280
18002EUR00.01260.00NaN0.0110600000NaN0NaN0NaN0NaN211280
18003GBP743.00NaN0NaN0.0171210010NaN0NaN0NaN0NaN211280